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Machine Learning Classification - Data Captured by Devices

Only highly Skilled Data Scientist.

I am dealing with Sensor Data which has numerical observations for different variables. I want to classify them into three labels A, B, C. But, here the situation is different. I have 30 Instances for training (or labelled data) and 20 instances for testing (unlabeled data).

Now, these instances are time series recordings for variables and parameters recorded by the sensor. Each instance is a CSV file. The name of the CSV file is the label, this label I need to classify. So, I have 30 CSV files or instances (or thirty observations for training ) and 20 instances or CSV files for testing.

I want to figure out, how can I put these data (each observation (or class) of training data is a csv file and each file contains 5000 observations) in a machine learning model to classify the unlabelled test data giving CSV file a input. Include ML/DL in your bid proposal to verify you read the proposal.

Compétences : Exploitation de Données, Data Scraping, Apprentissage Automatique, Neural Networks, Python

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Concernant l'employeur :
( 1 commentaire ) Kandhla, India

N° du projet : #15637247

22 freelance ont fait une offre moyenne de 179 $ pour ce travail

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4.8
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